Task-based tagging and classification of enterprise resources

ABSTRACT

Embodiments of the present invention relate to systems and methods for task-based tagging and resource classification, which allow tags or metadata to emerge from execution of work-related tasks and activities. In certain embodiments, tags can be automatically extracted from activities performed, for example utilizing a textual description of tasks carried out by an employee. Accumulated tags can then be utilized to describe enterprise resources. Automatic tagging or metadata annotation can be integrated with everyday work utilizing one or more techniques. Candidate tags can be extracted from a task written description utilizing an algorithm that analyzes keywords. Candidate tags can be refined, for example by clustering utilizing a K-means approach. Candidate tags can be ranked based on an overall frequency adjusted against time, with the importance of a tag declining with time.

BACKGROUND

The present invention relates to computing, and in particular, to asystems and methods for a computer implemented scheme for task-basedtagging and resource classification.

Unless otherwise indicated herein, the approaches described in thissection are not prior art to the claims in this application and are notadmitted to be prior art by inclusion in this section.

With the deepening of globalization—more and more non-mission-criticalbusinesses are outsourced away from the home countries. For example,example design teams may be located in Europe, manufacturers located inChina and service support located in India.

Thus it becomes increasingly important to establish the right teams ofspecialties for consultation, coordination, and collaboration. This canbe based on a thorough understanding of corporate human resource.

Competency management is considered an important measure for effectivelymanaging enterprise knowledge and human resources by way of resourceallocation, employee further development, etc. Effective competencymanagement helps to establish and maintain organizational knowledge.

This is of particular importance in the current global economy climate,as having a solid understanding of what a company currently processesand what knowledge is still missing and yet to be acquired, can help thecompany to better compete with others in the rapidly changing market.

The benefit is evident in two aspects. On the one hand, accurate andprecise self-evaluation leads to accurate market self-positioning, whichin turn facilitate business agility. When the market changes, a companycan quickly gather what it has and respond accordingly. Self-evaluationis inevitably grounded on pooling individual expertise into theorganizational competence directory.

On the other hand, organizations are currently facing the challenge ofpeople fluctuation (i.e. changing jobs become more common in the newworking population), as compared with earlier eras. Apart from thedirect cost (recruitment and training) of replacing those who leave theorganization, a more significant yet invisible cost is that more thanoften the knowledge of individual employees vanishes when people leavethe organization.

Organizations can take countermeasures to minimize the damage caused bysuch people fluctuation. Effective competency management can help reducethe chance of having individual employees as the critical path of toomany mission-critical business tasks.

Effectively managing enterprise resources has been approached fromdifferent directions. One approach is the Enterprise 2.0 paradigmdescribed by Andrew P. McAfee in “Enterprise 2.0: The dawn of emergentcollaboration”, MIT Sloan Management Review, 47(3):21-28 (2006), whichis incorporated by reference herein for all purposes. However, metadataof enterprise (information) resources is not always aligned with acompany's core business and the everyday working environment of theemployees.

Recently, tagging has also started to be applied in corporateapplications, including the area of competency management. Unlikegeneral social network web sites, the enterprise tagging approach, likemany other Enterprise applications, suffers from a lack of motivation inthe working environment. Sharing knowledge with follow colleagues is notalways highly appreciated and mutually beneficial especially when thecorporate culture does not practically reward such sharing.

Those individuals performing the tagging have to invest a significantamount of labor and time without obvious immediate benefits. It disturbsthe normal work routine and becomes less welcoming over the time. Thefear of losing power aggravates the situation.

Tagging colleagues as experts on certain topics does not necessarilyresult in a reciprocal merit action from the recommended due to onereason or another. This is also the underlying reason when tagging inenterprise environment works well in small scale pilot studies whereencouragement and requirement are endorsed by the management, but failto show long-term benefit when deployed in practice.

The present disclosure addresses these and other issues with systems andmethods for a computer implemented task-based scheme for tagging andresource classification.

SUMMARY

Embodiments of the present invention relate to systems and methods fortask-based tagging and resource classification, which allow tags ormetadata to emerge from execution of work-related tasks and activities.In certain embodiments, tags can be automatically extracted fromactivities performed, for example based upon a textual description oftasks carried out by an employee. Accumulated tags can then be utilizedto describe enterprise resources. Automatic tagging or metadataannotation can be integrated with everyday work utilizing one or moretechniques. Tags can be extracted from a task written descriptionutilizing an algorithm that identifies keywords. Tags can be refined,for example by clustering utilizing a K-means approach. Tags can beranked based on an overall frequency adjusted against time, with theimportance of a tag declining with time.

An embodiment of a computer-implemented method according to the presentinvention comprises, receiving a written description linked to a jobtask, extracting tags from the written description by text analysis andterm extraction, refining the tags by clustering, describing anenterprise resource using the tags, assessing a relevance of the tagsbased upon a date of task execution, storing the tags associated withthe enterprise resource, and managing the enterprise resource in amanner aligned with work activities.

In certain embodiments the written description refers to the enterpriseresource.

In certain embodiments the method further comprises storing the tagsassociated with the job task.

In certain embodiments the enterprise resource comprises a human being.

In certain embodiments the enterprise resource comprises a non-humanresource.

In certain embodiments the method further comprises preprocessing thewritten description with natural language processing methods prior tothe extracting.

In certain embodiments the written description is linked to the job taskthrough task patterning.

An embodiment of a non-transitory computer readable storage mediumaccording to the present invention embodies a computer program forperforming a method, said method comprising, receiving a writtendescription linked to a job task, extracting tags from the writtendescription by text analysis and term extraction, refining the tags byclustering, describing an enterprise resource using the tags, assessinga relevance of the tags based upon a date of task execution, and storingthe tags associated with the enterprise resource.

In certain embodiments the written description refers to the enterpriseresource.

In certain embodiments the method further comprises storing the tagsassociated with the job task.

In certain embodiments the enterprise resource comprises a human being.

In certain embodiments the enterprise resource comprises a non-humanresource.

In certain embodiments the method further comprises preprocessing thewritten description with natural language processing methods prior tothe extracting.

An embodiment of a computer system according to the present inventioncomprises one or more processors and a software program executable onsaid computer system, the software program configured to, receive awritten description linked to a job task, extract tags from the writtendescription by text analysis and term extraction, refine the tags byclustering, describe an enterprise resource using the tags, assess arelevance of the tags based upon a date of task execution, and store thetags associated with the enterprise resource.

In certain embodiments the written description refers to the enterpriseresource.

In certain embodiments the method further comprises storing the tagsassociated with the job task.

In certain embodiments the enterprise resource comprises a human being.

In certain embodiments the enterprise resource comprises a non-humanresource.

In certain embodiments the method further comprises preprocessing thewritten description with natural language processing methods prior tothe extracting.

In certain embodiments the written description is linked to the job taskthrough task patterning.

The following detailed description and accompanying drawings provide abetter understanding of the nature and advantages of the presentinvention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A shows a simplified flow chart of a method according to oneembodiment of the present invention.

FIG. 1B presents a graphic view of the process flow of FIG. 1A.

FIG. 2 shows a view of a task pattern editing environment.

FIG. 3 shows a view of a support ticket including patternable tasks.

FIG. 4 shows the evolution of a tag cloud according to an embodiment ofthe present invention.

FIG. 5 shows a computer system for use in a task-based enterprisetagging scheme according to one embodiment.

FIG. 6 illustrates hardware of a special purpose computing machineconfigured with a task-based enterprise tagging scheme according to oneembodiment of the present invention.

DETAILED DESCRIPTION

Described herein are techniques for providing a computer-implementedtask-based tagging and resource classification scheme.

The apparatuses, methods, and techniques described below may beimplemented as a computer program (software) executing on one or morecomputers. The computer program may further be stored on a computerreadable medium. The computer readable medium may include instructionsfor performing the processes described below.

In the following description, for purposes of explanation, examples andspecific details are set forth in order to provide a thoroughunderstanding of various embodiments of the present invention. It willbe evident, however, to one skilled in the art that the presentinvention as defined by the claims may include some or all of thefeatures in these examples alone or in combination with other featuresdescribed below, and may further include modifications and equivalentsof the features and concepts described herein.

One way of overcoming motivational dilemmas associated with tagging, isthrough combining automatic tagging with the work task execution. FIG.1A shows a simplified flowchart showing steps according to oneembodiment of a method 100 according to the present invention. FIG. 1Bshows this process flow in a more graphic form.

In a first step 102, a textual description of a work activity isprovided. This written description is linked in some way to a specifictask. In certain embodiments, this textual description may be formallycreated as part of standard job procedures, and may includepre-determined portions such as a task title, a subtitle, a summary, andtask body including references, descriptions of problems/solutions, anddiscussion threads. Examples of such written descriptions may includebut are not limited to work logs (such as time-entry logs), task tickets(such as technology support tickets), calendars, to-do lists, andexpense reports.

Alternatively or in combination with the creation of a formal writtendocument, the textual description may be created as a part of performingthe task itself. Examples of such written descriptions may include butare not limited to, emails, text messages, transcribed voicemails,meeting minutes, and documents specifically generated in furtherance ofthe task (such as memoranda, presentation slides, and other types ofdeliverables).

According to certain embodiments, the text description may also becreated based upon utilization of certain resources of the enterprise.In certain embodiments, these resources may be human or non-human innature. As used herein, the term “artifact” refers generally to aresource of the enterprise that is non-human in nature, for example asoftware program, a document, a website, a policy, or a procedure.Examples of written descriptions arising out of reliance upon enterpriseresources may include but are not limited to, browser logs, phone logs,discussion forums, software application use (check-out) histories, anddocument preparation template access histories.

According to certain embodiments, the creation of a written descriptionthat is linked to a task, can be facilitated utilizing a task patterningapproach. This is described below in connection with FIG. 2.

In a second step 104, a tag is automatically extracted from the textualdescription. Certain embodiments may perform this tag extraction throughanalysis of keywords present in the written description. An example ofsuch an algorithm to perform tag extraction based upon keywordextraction, is described below and in conjunction with FIG. 3.

In a third step 106, tags are refined for relevance based upon one ormore approaches. According to certain embodiments, the relevance of atag may be compared with other tags based upon clustering techniques,for example k-means clustering. By refinement in such manner, the sizeof tag corpus can be reduced/consolidated into a set of final tags.

As shown in a fourth step 108, the tags that have been created are usedto describe resources of the enterprise. Examples of resources includehuman expertise, as well as non-human resources present in theinfrastructure of the enterprise.

As shown in a fifth step 110, tags used to annotate an enterpriseresource can be ranked according to their relevance. In certainembodiments, such tag ranking can be based upon task history (with morefrequently used tags assigned a higher ranking than those lessfrequently used ones), and can be based upon time (with more recent tagsassigned a higher ranking than older tags based upon their continuingrelevance).

In a sixth step 112, various resources (both human and non-human) of theenterprise, can be effectively managed utilizing the tags. As discussedfurther below, one exemplary use for such tags in the enterpriseenvironment is for resource analysis; that is, detecting the realmeaning of resources.

Another possible use for the tags is in recommending resources, forexample locating human experts on particular subjects within anorganization. Conversely, analysis of the tags can also be used toidentify areas of expertise that are lacking within the enterprise.

Still another potential use for tags is in understanding the currententerprise knowledge landscape. This allows identification of particularresources that are available within an organization and help inenterprise competency management.

Yet another example of a possible use for tags, is to discovermisalignment of resources and current work practices. This can be doneby analyzing patterns in the association of work behavior withparticular enterprise resources as described by tags.

As mentioned above, certain embodiments may rely at least in part uponextraction of tags from written descriptions linked to specific tasksthrough the concept of task patterning. U. V. Riss, A. Rickayzen, H.Maus, and W. M. P. van der Aalst, “Challenges for business process andtask management”, Journal of Universal Knowledge Management, SpecialIssue on Knowledge Infrastructures for the Support of KnowledgeIntensive Business Processes, pages 77-100 (2005), is incorporated byreference herein for all purposes.

In this document, the task pattern approach was suggested as a low costway to capture process knowledge in knowledge-intensive work. Thisapproach involved the dissection of process models on the level ofindividual tasks, and their use to record and abstract activitiesnecessary to fulfill the tasks. By doing so, the sharing of processknowledge becomes well focused and grounded as sharable and reusablepatterns of tasks.

With a task pattern, task execution can be faithfully recorded togetherwith human and physical resources used during the execution of tasks.This association between work tasks and enterprise resources offers asolid ground for the task-centric enterprise resource tagging accordingto embodiments of the present invention.

Embodiments of tagging schemes according to the present invention can beapplied to virtually any resources referred to in task execution. Theseinclude individuals, departments, policies, documents, and others.However, the following discussion focuses mainly upon how such atask-based tagging scheme can facilitate competency management.Similarly, embodiments of the present invention can achieve managementof other types of resources.

Approaches and goals for competency management are described by TobiasLey, Dietrich Albert, and Stefanie Lindstaedt in “Competency managementusing the competence performance approach: Modeling, assessment,validation, and use”, Competencies in Organizational E-Learning,Miguel-Angel Sicilia, Ed., pp. 83-119. Information Science Publishing,Hershey, Pa. (2006), which is incorporated by reference in its entiretyherein. These arguments in favor of competency management include: i)support strategy planning and align the core business with thestrategies, ii) increase the awareness of competency across differentunits within an organization, and iii) further development of bothindividuals and teams/departments.

Competency management is traditionally performed through self-assessmentby the employees. Sometimes, this is enhanced with guidance and/orsupervision from the management. For instance, employees may be requiredto label themselves with keywords drawn from a predefined vocabulary.

The corpora from which labels may be chosen, may be manually craftedwith the help of domain specialists, and repetitively updated to reflectthe core business and strategies of organizations. Such a guidedself-assessment, though assuring alignment between individual andorganizational perspectives, might fail due to the inaccuracy resultedfrom deliberate exaggeration and understatement and/or intentional orunintentional misinterpretation of the vocabulary.

In order to minimize the influence of such “self” factors in specialistprofiling, people tagging was proposed by Simone Braun, ChristineKunzmann, and Andreas Schmidt in “People tagging & onto-logy maturing:Towards collaborative competence management”, CSCW to Web2.0: EuropeanDevelopments in Collaborative Design Selected Papers from COOP08,Computer Supported Cooperative Work, David Randall and Pascal Salembier,Eds, Springer, Berlin/Heidelberg (2010), which is incorporated byreference for all purposes.

A concept underlying such people tagging is to combine tagging andsemantic web technologies in peer assessment. That is, employees tageach other according to domain ontologies.

Although in pilot studies people tagging has shown interesting results,it does not provide answers to the inherent weakness of manual labelingand tagging approaches: lack of fairness and low maintainability. Forexample individuals may over- or under-assess others, due topeer-pressure or conflict of interests. Meanwhile, maintaining a largenumber of tags is time and labor-intensive, while the immediate value oftagging is not clearly evident.

Automatic extraction of an individual's competency profile, is a validalternative. A number of efforts have been directed to this approach.For example in the academic environment, publications have been used toelicit expertise of the authors, as described by R. Crowder, G. Hughes,and W. Hall in “Approaches to locating expertise using corporateknowledge”, International Journal of Intelligent Systems in Accounting,Finance, and Management, 11(4):185-200 (2002), which is incorporated byreference in its entirety for all purposes.

In a collaborative workspace, expertise can be extracted from thematerials contributed from individuals. This is described by MarkMaybury, Ray D'Amore, and David House in “Expert finding forcollaborative virtual environments”, Commun ACM, 44(12):55-56 (2001),which is incorporated by reference in its entirety for all purposes.

In the aforementioned approaches (manual and automated), profiling isstill largely decoupled from an individual's work context. Even thoughautomatic methods are less subjective, isolation from actual workactivities (which might be due to too coarse or too fine of thegranularity levels) jeopardizes the usefulness of the resultantcompetency profiles. Accordingly, a mapping tool is helpful to identifythe correspondences among different granularity levels.

Furthermore, co-authorship of publications and other documents does notnecessarily reflect who has contributed to what part of those artifacts.In certain circumstances, article co-authors may be named based uponconsiderations other than specific contributions.

A similar gap may be present in the tagging and management of otherenterprise resources, for example where an apparatus inspecting theresources does not align them with the work activities where theresources are consumed. Inaccuracy may result from management toolsrelying on the erroneous metadata.

In a real-world environment, expertise is essentially boosted throughcontinuous practice, while certain skills die out without a practicaldemand for them. An employee's competency can therefore be profiled byfaithfully capturing everyday work activities of the employee, as isachieved by embodiments according to the present invention.

Certain embodiments may leverage the concept of the task pattern as adata source. Tagging can be combined with work activities as describedin the Example. Possible technologies to facilitate task-based taggingare also described. Tagging schemes according to embodiments of thepresent invention may give rise to more effective management of humanresources and artifacts of the enterprise.

The concepts of tasks, task patterns, and processes are described asfollows. A process is a collection of structured activities (tasks) witha precise goal to be achieved over a period of time. The activities(tasks) of a process are partially ordered and can be further dividedinto finer-grained sub-tasks.

A task is an action requiring completion. A task pattern is anabstraction of tasks replacing specific resources with abstractors. Taskpatterns can be instantiated by assigning concrete instances to a taskresource abstractor as the abstraction of artifacts and human resourceassociated with tasks. This is described by Riss et al. as cited above.

The task pattern approach is bottom-up, in that it involves users in thecreation and sharing of process (and process related knowledge), withoutnecessarily implicating them in actual business process managementactivities. This is done through task-based experience reuse or taskabstraction, transferring users' experience (acquired after successfullyaccomplishing certain tasks), into manageable and operable formulae.

At least two options exist for realizing a task abstraction operation.First, entire task structures and details can be duplicated, with theassumption that everything is implicitly relevant to the next task in asimilar context. Second, users are given the responsibility ofexplicitly selecting every detail to be documented.

In practice, the former is likely to be useful only for a small set oftasks. This is because when task number increases, so does informationto be considered. Information overload leads to a situation that usersspend potentially more effort customizing the duplicated task than tostart a new one.

The latter option could also overwhelm users as it requires them toconsider too many details, some of which might not be reusable. Usersmight also misjudge the significance of certain information making therecorded experience incomplete.

Accordingly, a more helpful position is to consider the reuse of pastexperience lies somewhere in between these two extremes. To that end,the concept of task pattern was proposed as the basis upon whichprevious experience can be shared.

Task patterns are the records of previous task activities andinformation artifacts. Task patterns are harvested by monitoring theinteraction between users and a task management system. This allowscollecting valuable information of events during task execution, andgeneralizing the information into resource abstractors.

The task history actually provides an explicit view on how the task iscompleted with critical information artifacts attached to it. Thetransition between task and task pattern is supported in a task patternmanagement system, as shown in FIG. 2.

In particular, FIG. 2 shows a user interface 200 wherein a taskpatterning software application facilitates the creation of a writtendescription 202 in connection with a work-related task. This writtendescription can be used to as a basis for the extraction of tagsaccording to embodiments of the present invention.

A task pattern mainly serves as a record of work activities. It fillsthe gap in situations where such activities are not readily documented.

Superficially, the use of task pattern appears to introduce extra workto employees by adding a layer of formality. However, this is notnecessarily the case.

In the enterprise environment, work related activities are normallyprescribed by directive and regulatory guidelines, with employeesimplicitly and/or explicitly leaving evidence of how they accomplish atask. For example, having received a task, the typical next step is tostart with looking for organizational regulations and protocols thatformally specify how to proceed. Colleagues who seem to have previousexperience can be consulted; contributions from the others aresolicited.

Communication with other contributors occurs more often throughtext-based methods (e.g. e-mails, memorandums, reports, and other typesof documentations) than vocal-based ones. Decision-making procedures aredocumented for quality control and auditing purposes. This isparticularly evident in customer support units where tasks are in theform of tickets raised by users and are handled throughproblem-solutions and asynchronous discussions documented as part of theticket history.

Thus below the surface, the use of task patterns or task journalsalready exists naturally in the everyday working environment where tasksas performed involving more than one employee. The existence of taskpatterns becomes more evident in examining geographically distributedorganizations and customer-facing departments.

For instance, customer support widely uses ticketing systems to interactwith customers and keep record for reviewing. Each of the ticketpresents a instance where task pattern can be extracted.

EXAMPLE

The translation of tasks to tags may be demonstrated utilizing thesupport ticket system as an example. Each ticket presents a wellstructured task with ticket subject (as task title), short summary, andticket description.

Keywords emerging from the content of tickets are a good source of tags.Associated with each ticket are ticket owners, contributors (appearingas email recipients, discussion participants and delegates for tasks)and other artifacts (as web pages, documents, and other tickets) thatare referred to when the current ticket is being tackled. Management ofthese associated resources can benefit from being explicitly annotatedwith the tags derived from the ticket/task.

During the process of handling a system ticket, support staff tend toproceed as follows. First, they accept the ticket and assume the ticketownership. Next, they go through the content of the ticket and recall ifsimilar tickets were encountered previously. They may redirect/delegatethe ticket to some one that is more suitable to handle the problempresented in the ticket.

If the ticket owners decide to proceed, they browse internal and publicresource repository for possible references. They attach the email replywith a link to the selected material if it is in public domain or a casenote if it is an internal document. After email exchange(s), they eithermark the ticket as solved or request the customers to take furtheractions.

Embodiments of the present invention may naturally embed the taggingprocess in employees' normal work routine, so as to be as lessobstructive as possible. For simplicity, some embodiments according tothe present invention may build upon an implemented task-patternmanagement system, one of example of which is the Kasimir userinterface. The Kasimir user interface is described inhttp://nepomuk.semanticdesktop.org, which is incorporated by referencefor all purposes. Kasimir automatically records actions performed whencarrying out a task.

While this embodiment is described in connection with Kasimir, thepresent invention is not so limited, and task-centric tagging can beapplied to extend any systems that record the use of resources,communication, and delegation/collaboration when performing tasks, e.g.the aforementioned ticketing systems.

Often, parts of a task may be redirected to colleagues or businesspartners. This is done as task delegating.

When delegating a task or part of a task (T) to another colleague (P),an association is essentially established between the colleague and T.This implies that the task owner trust this colleague as an expert onwhatever problems presented in T.

If P successfully performed T, tags extracted from T then capture theexpertise or experiences that P may acquire through participating in T.Values such as accumulated overtime and relative frequency of tagoccurrence, indicates the strong and weak points of an employee'scompetence.

It also possible to feed this information into enterprise expertfinders. Such an enterprise expert finder can match task descriptionsagainst profiles of employees, so as to suggest candidates forcollaboration and team building.

Artifacts may be referenced as follows. When trying to complete a task,support staff tend to cite links to other useful (internal or external)resources as evidence to support observations or solutions. Thistask-resource relation furnishes a new approach to establishingcollaborative understanding of enterprise artifacts.

By connecting artifacts to a task (T), one explicitly annotates theartifacts with tags extracted from T. Again, after a certain period ofproductive use, by studying the tags, real semantics of these artifactsgradually emerge.

The emergent semantics demonstrate how individuals see the artifacts inpractice. It might differ from what are suggested by the titles of theartifacts, and even different from the intended semantics of theiroriginal creators.

Represented as a vector of tags, we can leverage the emergent semanticsof enterprise artifacts in resource recommendation and resourcemanagement. For instance, similarity between artifacts can be computedbased on the tag vectors using popular measures such as cosinesimilarity, Manhattan distance, ontology based similarity, string basedsimilarity, or geometric distance.

Tag/Keyword extraction may occur as follows. Tags used in the aboveprocess can be acquired either manually or automatically.

Thus far, manual tagging is widely used in and has become a signature ofmany social network websites, e.g. Flickr, Delicious, and CiteULike. Itrapidly gained popularity due to the Web 2.0 phenomenon. The outcome ofsuch an activity, known as folksonomies, presents a low overhead and lowmaintenance cognitive consensus of the “crowds”.

Recently, efforts have been made to bring collaborative manual tagginginto the enterprise environment so as to help in enterpriseresource/content management. An example is described by Ajita John andDoree Seligmann in “Collaborative tagging and expertise in theenterprise”, Proceedings of Collaborative Web Tagging Workshop held inConjunction with WWW 2006 (2006), which is incorporated by reference inits entirety herein.

Here, certain embodiments of the present invention may avoid manualtagging and leverage the more conventional automatic keyword extractionmethods to minimize subjective biases. Keywords extracted from taskcontents are considered equivalent to tags, and these two terms are usedinterchangeably herein.

Automatic keyword extraction emphasizes an operable and systematicdetermination of words that are the most important ones in a document.It taps in linguistic, statistic, data mining, and semantic webtechnologies, to boil down documents into a set of most representativewords.

The following discussion focuses upon single document keywordextraction. This is due to both theoretical and practicalconsiderations.

On one hand, employees' expertise and experience change along withindividual tasks that they accomplish. Keyword extraction based onmultiple documents may not faithfully reflect such subtle fluctuationonly visible to individual tasks.

On the other hand, periodically updating keywords across a large corporaof tasks might be difficult in enterprise environment due to data safetyand privacy concerns. For instance, contractual materials might have tobe eliminated physically and electronically when partnership ceases tocontinue. Operational documents might be taken offline when they becomeobsolete. Access privileges to certain customer data might be revokedafter one finishes dealing with the customer. All these may interferewith drawing keywords from a large number of tasks, even thoughmulti-document extraction outperforms methods based on single document.

After evaluating several keyword extraction algorithms on task data, thealgorithm from the following reference was adopted: Yutaka Matsuo andMitsuru Ishizuka, “Keyword extraction from a single document using wordco-occurrence statistical information”, International Journal onArtificial Intelligence Tools, 13:2004 (2003), which is incorporated byreference in its entirety herein for all purposes.

In this approach, one detects the significance of a term T with respectto a document D, based on whether or not the co-occurrence probabilityof T and the N most frequent terms N_(T) agrees with the baselineprobability distribution of N_(T). If the divergence is significantenough, T is added to the initial tag/keyword set comprising the termsin N_(T).

In the Matsuo and Ishizuka paper, the text content subject to keywordextraction includes task title, subtitle, summary if applicable and taskbody (e.g. problem/solution descriptions, discussion threads). However,embodiments of the invention do not crawl along embedded links to otherresources that are referred to in task descriptions.

Text for keyword/tag extraction can be preprocessed with naturallanguage processing methods, such as stemming, stop-word removal, etc.For instance, FIG. 3 shows a task ticket 300 from which tags may beextracted. This task ticket includes an identification 301 of a humancontributor, marked with a circled number one.

In FIG. 3, the text content 302 of the ticket is highlighted with dashedrectangle and marked with a circled number two. This text content alsoincludes a reference 304 to other enterprise resources, indicated with acircled number three. Applying the keyword extraction algorithm to thisexample will result in generation of the following tags: “revenue”,“recognition”, “COGS”, “cost”, “workcenter”.

Task titles and descriptions are rarely subject to formal review andproof-reading. Therefore, synonyms, acronyms, and even typos abound,depending on the habit of individual task issuers/handlers. Forinstance, one issuer might consistently use “service-orientedarchitecture”, while another prefers the shorthand version “SOA”. Athird user may employ a combination of both terms.

Thus, the resultant corpus of tags can become noisy after an extensiveperiod of productive use. Housekeeping of tags, therefore, may beuseful. Accordingly, certain embodiments of the present invention mayperform tag clustering to facilitate their use, by reducing the size oftag corpus.

Tag (term) clustering is a well researched area. It finds a basis inInformation Retrieval (IR) and applications in life science, multimedia,etc. Examples are described by A. K. Jain, M. N. Murry, and P. J. Flynn,“Data Clustering: A Review”, ACM Computing Surveys, Vol. 31, No. 3(September 1999), and by Todd E. Scheetz, Nishank Trivedi, Kevin T.Pedretti, Terry A. Braun, and Thomas L. Casavant, “Gene transcriptclustering: a comparison of parallel approaches”, Future Gener. Comput.Syst., 21(5):731-735 (2005), both of which are incorporated by referencein their entireties herein for all purposes.

In order to achieve the best results, certain embodiments of the presentinvention may adopt the bisecting k-means algorithm. This algorithm hasbeen demonstrated to outperform other popular clustering algorithms.This is described by Michael Steinbach, George Karypis, and Vipin Kumarin “A comparison of document clustering techniques”, Technical report(2000), #00-034 available at http://www.cs.umn.edu/tech reports, whichis incorporated by reference in its entirety herein.

As explained in that document, bisecting k-means is a simple andefficient variant of the basic k-means algorithm. It starts withrepetitively splitting an arbitrary cluster into two using the basick-means algorithm. The split with the highest in-cluster overallsimilarity is accepted, marking the end of one loop. The desired numberof clusters is achieved by further splitting the clustering with thesame bisecting approach.

In applying the k-means algorithm, some embodiments of the presentinvention may compute the dissimilarity between two keywords/tags withthe symmetrized and smoothed version of Kullback-Leibler divergence. Thedissimilarity of two keywords/tags, x and y, is the divergence of theco-occurrence probabilities of x and y with respect to all the othertags extracted from the tasks that were performed by an employee:

$\begin{matrix}{{{dissim}_{js}\left( {x,y} \right)} = {\frac{1}{2}\left( {{\sum\limits_{i}{p_{\langle{x,t_{i}}\rangle}\log\;\frac{p_{\langle{x,t_{i}}\rangle}}{M}}} + {\sum\limits_{i}{p_{\langle{y,t_{i}}\rangle}\log\;\frac{p_{\langle{y,t_{i}}\rangle}}{M}}}} \right)}} & (1) \\{M = {\frac{1}{2}\left( {p_{\langle{x,t_{i}}\rangle} + p_{\langle{y,t_{i}}\rangle}} \right)}} & (2)\end{matrix}$where

is the probability of co-occurrence of tags x and t_(i).

An underlying rationale of this dissimilarity measure is that tags whoseco-occurrences with others are highly agreed with each other are likelyto be semantically associated. For instance, “SOA” tends to appeartogether with e.g. “SOAP”, “rest(ful)”, “web service”, etc. where thefrequency of co-occurrence agrees with those of the phrase “serviceoriented architecture”.

In practice, the clustering algorithm may be reinforced with simpleheuristics drawn from public domain, e.g. Wikipedia English Version2.Testing of the clustering algorithm with 150 keywords yielded thefollowing preliminary results.

The algorithm is able to identify alternative names (e.g. “colgate”versus “colgate-palmolive”). The algorithm is also able to identifyfrequently used acronyms (e.g. “CC” versus “Credit Card” and “PwC”versus “PriceWaterhouseCoopers”).

Human supervision may not be entirely excluded. For example, recommendedclusters may be validated and verified by domain experts.

In addition, the algorithm relies on a major assumption that differentcontributors may consistently largely use the same variants within atask. Failing to meet the prerequisite can render the clusteringapproach less useful. Evaluating with large test data sets may also beimportant.

Task-based profiling and competency tagging according to embodiments ofthe present invention may possess a dynamic feature. In certainembodiments, tags may be ranked as follows.

Individuals take up new tasks and acquire new expertise from performingthe tasks. In the meantime, the skills gained from out-dated tasksbecome less proficient and even obsolete.

Therefore in certain embodiments the corpus of tags indicating employeecompetence, may evolve based on their recentness, so as to give a higherweight to recent and more up-to-date tags then historical ones. Aftertaking into account the time dimension, certain embodiments adopt theexponential decay factor and define the final tag weight as the sum oftag occurrence adjusted against time according to Eq. (3):

$\begin{matrix}{{\omega(\alpha)} = {\sum\limits_{T \in \tau}{{\mathbb{e}}^{- {\lambda{({t_{0} - t_{T}})}}} \cdot {{Occ}_{T}(\alpha)}}}} & (3)\end{matrix}$where λ is for tuning the weight against particular applications; t₀represents the current time; t_(T) is the time when the task T isperformed; τ is the set of tasks performed by an employee; andOcc_(T)(α) is the occurrence of tag a that is drawn from T.

Note that λ value should be adjusted according to the specific need ofapplications. In a majority of the cases, it is assumed that λ=1.

FIG. 4 shows a tag cloud 400 evolving along time. In particular, themonitor “freeze” 402 that has been drawn from an old ticket/task,gradually disappears as time passes. The weights of other tags may beenhanced over time, as denoted by changing indicia such as their fontsize or font color.

Embodiments of the present invention also allow management of theenterprise resource using tags. Enterprise IR becomes a prevalentchallenge with rapidly falling storage and digitizing cost, leading tolarge volumes of enterprise information artifacts being available inelectronic form.

Searching into the enterprise information repositories may be inherentlydifferent from general web search and web IR. Accordingly, suchsearching may call for well tuned, unique solutions. Examples of suchsolutions are described by Lars Ahrenberg in “Term extraction: A Review,Draft Version 091221”, Linkoping University, Department of Computer andInformation Science, and by David Hawking in “Challenges in enterprisesearch”, ADC '04: Proceedings of the 15th Australasian databaseconference, pp. 15-24, Australian Computer Society, Inc. (2004), both ofwhich are incorporated by reference herein for all purposes.

Instances of differences between general searching and searchingenterprise information, are demonstrated in the following aspects.First, effective enterprise search algorithms may often rely uponthorough indexing mechanisms. The non-transparent and proprietary natureof enterprise information, however, deteriorates the performance of manymachine learning and data mining algorithms that show excellent resultson indexing the “open” World Wide Web.

Second, enterprise information artifacts are heterogeneous in format.Effective IR methods should operate on a unified indexing scheme over awide variety of enterprise resources such as artifacts, people,divisions, and geographic regions.

A third aspect is that enterprise IR should accommodate queries composedusing local business languages. Bound tightly with the corporateculture, each company to some extent exercises a business dialect whichmay not be “understandable” to general IR methods.

Indexing enterprise information resources is tantamount to assigningmetadata to such resources. When assigning metadata to enterpriseresources, there is always a risk of misalignment. One example ismisalignment between the intension (semantics) of the resources and howpeople use them in practice. Another example is misalignment between thecontext where the resources are annotated and the context where theresources are consumed. Still another example is misalignment betweenmetadata annotation tools and one's everyday working environment.Embodiments of the invention can address/alleviate such misalignment,and situate enterprise resources annotation in everyday work.

A task-based tagging approach according to embodiments of the presentinvention can aid in enterprise resource management, with a workspecific indexing mechanism.

According to certain embodiments, the tag-based schema can be utilizedto identify experts. The expertise of employees can be effectivelycaptured with a set of task-based tags. When trying to locate the mostappropriate expert for a problem at hand, one can then rank all theemployees based on how good they can take on the job.

If both the employees' competence tags and the problem descriptors areconsidered as vectors in a high dimensional space, the appropriatenessof an employee against a given problem can be computed according toEquation (4):

$\begin{matrix}{{{sim}\left( {p,e} \right)} = \frac{\sum\limits_{i}{w_{p_{i}} \times w_{e_{i\;}}}}{\sqrt{\sum\limits_{i}w_{p_{i}}^{2}}\sqrt{\sum\limits_{i}w_{e_{i}}^{2}}}} & (4)\end{matrix}$where w_(p) _(i) and w_(e) _(i) are the weights of keywords/tags in theproblem description and employees' competence tags respectively.

Enterprise expert finding systems can then utilize this competencytagging approach with other graphic user interface features. Forinstance, the system can automatically recommend the top ten bestmatching candidates for users to evaluate and decide.

Task-based tagging can be applied to the management of other types ofenterprise resources. According to certain embodiments, the tag-basedschema can be utilized to provide a system for recommending resources.

Similar to expert finder, tags associated with artifacts such asdocuments, web pages, etc., facilitate automatic resource recommendationbased on a distance measure as discussed above. That is, among all thepossible candidates, the system according to embodiments of the presentinvention may find the best match, and suggest same to users togetherwith (dis)similarity. An advantage of this recommendation is that it issituated with work context, and interprets similarity of artifactsindependent of their face value.

If the concept of “artifacts” is extended to tasks and processes, therecommendation system can be based on similarity computed as follows:

a task comprises sub-tasks, and is associated with artifacts and humanresource.

This naturally becomes a labeled and directed graph, G=(V,E) with nodesV corresponding to (sub-)tasks and individual artifacts, and edges Ecorresponding to either part-whole relationships or resourceassociations.

The easy conversion of task graphs inspires us to consider graphsimilarity measures. Graph similarity has been studied, for example byL. Lovasz and M. Plummer in “Matching Theory”, North-Holland, Amsterdam(1986), which is incorporated by reference herein for all purposes.

A task graph is essentially a tree. The root of a task tree is the taskitself. Children of the root are the first level sub-tasks, which inturn have their sub-tasks as child nodes. The leaves of a task-tree areartifacts supporting the fulfillment of the task.

A piece of an artifact may be duplicated when it is referred to by morethan one sub-task. Focusing on task trees allows how a subtask node islabeled, to be largely ignored.

When two sub-tasks are supported by the same set of evidence, similarityof leaves is computed as above. It can be understood, based on theclosed world assumption, they have overlapping instance data and use thesame knowledge to proceed.

This leads to a further assumption that subtask using the sameknowledge, can be considered as similar ones even though they arelabeled differently. The closed world assumption is supported by twoobservations in enterprise environment.

First, the supporting evidence is shared and frequently used by a largenumber of employees. Thus, a common understanding of tags can be easilynegotiated and reinforced.

Second, such a set of artifacts is relatively stable. The creation orintroduction of new information artifacts in a mature organization, isconstrained by protocols and regulations. Hence information artifactsthat are not present, are considered to be excluded from theorganizational knowledge space and from the similarity computation.

According to certain embodiments, the algorithm can be formalized as:

$\begin{matrix}{{{sim}\left( {\tau,\tau^{\prime}} \right)} = \frac{{{\gamma(\tau)}\bigcap{\gamma\left( \tau^{\prime} \right)}}}{{{\gamma(\tau)}\bigcup{\gamma\left( \tau^{\prime} \right)}}}} & (5)\end{matrix}$where γ(x) gives the set of supporting artifacts of x, and τ and τ′ aretwo bottom-level subtasks.

For pairs of inner nodes from different task trees (T and T′), thesimilarity is computed from those of their children using tree editdistance. This is described by J. Wang, K. Zhang, K. Jeong, and D.Shasha in “A system for approximate tree matching”, IEEE Transactions onKnowledge Data Engineering, 6(4):559-571 (1994), which is incorporatedby reference in its entirety for all purposes.

Tree edit distance is an approximate measure computing the difference oftwo trees as a numeric value between 0 and 1. Based on the TreeDiffalgorithm, task tree edit distance is defined as the minimum number ofnode deletes and inserts when one task tree is transformed into another.sim(T,T′)=1−diff(T,T′)diff(T,T′)=min{ε(S)|S is a sequence of edit operations T→T′}where ε(·) is the cost function mapping an edit operation to a numericvalue based on users' preference. The initial alignment among nodes,stems from the similarity among bottom-level subtask nodes computed asabove.

FIG. 5 illustrates hardware of a special purpose computing machineconfigured to implement a tagging scheme according to an embodiment ofthe present invention. In particular, computer system 501 comprises aprocessor 502 that is in electronic communication with acomputer-readable storage medium 503 (e.g. a non-transitory computerreadable storage medium) that can comprise code for performing certainaspects of embodiments of the present invention.

For example, the processor may include a task patterning module 504 thatis configured to generate a task description 510 that is stored. A tagextraction module 506 may reference the task description and generate atag 512 that is stored. A module 508 configured to perform tagassignment and tag management, may reference the stored tag and thenstore the tagged enterprise resources 514.

The computer system may comprise a software server. A number of softwareservers together may form a cluster, or logical network of computersystems programmed with software programs that communicate with eachother and work together to process requests.

An example computer system 610 is illustrated in FIG. 6. Computer system610 includes a bus 605 or other communication mechanism forcommunicating information, and a processor 601 coupled with bus 605 forprocessing information.

Computer system 610 also includes a memory 602 coupled to bus 605 forstoring information and instructions to be executed by processor 601,including information and instructions for performing the techniquesdescribed above, for example. This memory may also be used for storingvariables or other intermediate information during execution ofinstructions to be executed by processor 601. Possible implementationsof this memory may be, but are not limited to, random access memory(RAM), read only memory (ROM), or both.

A storage device 603 is also provided for storing information andinstructions. Common forms of storage devices include, for example, ahard drive, a magnetic disk, an optical disk, a CD-ROM, a DVD, a flashmemory, a USB memory card, or any other medium from which a computer canread.

Storage device 603 may include source code, binary code, or softwarefiles for performing the techniques above, for example. Storage deviceand memory are both examples of non-transitory computer readable storagemedia.

Computer system 610 may be coupled via bus 605 to a display 612, such asa cathode ray tube (CRT) or liquid crystal display (LCD), for displayinginformation to a computer user. An input device 611 such as a keyboardand/or mouse is coupled to bus 605 for communicating information andcommand selections from the user to processor 601. The combination ofthese components allows the user to communicate with the system. In somesystems, bus 605 may be divided into multiple specialized buses.

Computer system 610 also includes a network interface 604 coupled withbus 605. Network interface 604 may provide two-way data communicationbetween computer system 610 and the local network 620. The networkinterface 604 may be a digital subscriber line (DSL) or a modem toprovide data communication connection over a telephone line, forexample. Another example of the network interface is a local areanetwork (LAN) card to provide a data communication connection to acompatible LAN. Wireless links are another example. In any suchimplementation, network interface 604 sends and receives electrical,electromagnetic, or optical signals that carry digital data streamsrepresenting various types of information.

Computer system 610 can send and receive information, including messagesor other interface actions, through the network interface 604 across alocal network 620, an Intranet, or the Internet 630. For a localnetwork, computer system 610 may communicate with a plurality of othercomputer machines, such as server 615. Accordingly, computer system 610and server computer systems represented by server 615 may form a cloudcomputing network, which may be programmed with processes describedherein.

In an example involving the Internet, software components or servicesmay reside on multiple different computer systems 610 or servers 631-635across the network. The processes described above may be implemented onone or more servers, for example. A server 631 may transmit actions ormessages from one component, through Internet 630, local network 620,and network interface 604 to a component on computer system 610. Thesoftware components and processes described above may be implemented onany computer system and send and/or receive information across anetwork, for example.

Tag-based schemes according to embodiments of the present invention mayoffer a number of possible benefits. One such benefit is to motivateemployees.

As discussed in the previous sections, one of the major barriers tosuccessfully deploying and exploiting a tagging system in the enterpriseenvironment can be the lack of incentive. The past few years havewitnessed the rise and fall of so-called enterprise 2.0 platforms.Initial excitement faded when the attention from management has beendiverted to other more urgent businesses, and when “try-out” has becomework routine. Unless such tools becomes an integral part of one's dailyworking environment, it is not likely to maintain the same level ofenthusiasm in the long term.

Embodiments of the present invention address this by automating thetagging process and making it integrate within employees' everyday work.First, tagging is automatically done as part of one's work.

The motivation of creating and maintaining tags is then driven by themotivation of accomplishing one's work. For the latter, the motivationis naturally assured in companies.

Second, the accuracy of tags is assured. The desire of performing worktasks with good quality leads to accurate and precise links between taskand support documents as well as colleagues as human experts that oneleveraged to complete the tasks. Misconduct, spam, and otherinappropriate behavior widely witnessed in general social networkweb-sites, therefore, is less likely to appear in this task-basedsituations.

After a period of productive use, further benefits may emerge. Theseinclude but are not limited to the following application scenarios.

Aligning employees' expertise with their work: misaligned expertise andwork requirements is one of the reasons that lower the performance ofemployees. Many organizations exercise and encourage people to evaluatethemselves through self-labeling. When tags accumulated with thetask-based approach is deviated too much from his/her self-evaluation,it suggests that some changes have to be made, either transferring theemployee to another unit/department or providing corresponding training.

Aligning employees' expertise with organizational strategies: taggingapproaches according to embodiments of the present invention may beclosely related to the actual business of a company (i.e. the dailyactivity of the employees). The tendency can be clearly visualized (e.g.with tag cloud). The management can then easily discover whether theactually daily activities deviate from the company's core business andwhether the company should be reorganized to gain efficiency. Forinstance, if the general clergy staff has to handle a large number oftrip booking tasks, it might suggest that a dedicated travel managershould be appointed to acquire and improve the corresponding expertise.

Housekeeping enterprise resources: it might be the case that someenterprise resources have been associated with apparently irrelevant ormismatching tasks. This can result from the resources either beingwrongly interpreted, or the resources are given a wrong description. Inboth cases, some housekeeping is necessary. On the other hand, ifdifferent resources are constantly appear in similar contexts, itsuggests the existence of duplication. Of course some duplication mightbe due to operational needs, highlighting it can draw people's attentionfor further investigation. Finally, if certain physical or humanresources are seldom referred to in employees' every tasks, redundancyto some extent is suggested. It is therefore beneficial to re-align theworkforce or the organizational knowledge repository, in terms ofdocuments, regulations, and protocols, to reflect the actually businessof a company.

In conclusion, tagging as the signature of Web 2.0 era, has beenintroduced to enterprise content management and enterprise search.Embodiments of the present invention relate to methods and systems whichembed enterprise tagging into the everyday working environment. This isachieved by utilizing the textual description of tasks that have beensuccessfully carried out by an employee. Keywords extracted from suchtask descriptions recapitulate an employee's past experience and thusthe expertise that he/she may/can acquire through accomplishing thetasks.

Intuitively, this tagging approach alleviates the issues intrinsic tomanual tagging approaches, for example a lack of incentive, subjectiveunder/overstatement, mismatch of granularity levels, and difficulty ofmaintenance. Embodiments may be used to enhance current enterprisecontent management as well as competency management capacities, forexample collaborative people tagging, and automatic expert profiling.Embodiments of the present invention can complement existing approachesto achieve a more accurate alignment with one's everyday work-relatedactivities.

The above description illustrates various embodiments of the presentinvention along with examples of how aspects of the present inventionmay be implemented. The above examples and embodiments should not bedeemed to be the only embodiments, and are presented to illustrate theflexibility and advantages of the present invention.

For example, keyword manipulation algorithms proposed in the paper arejust exemplary implementations, and others are possible. Alternativeapproaches to the optimization of keyword management may be used. Forinstance, a domain ontology can be leveraged to give guidance to thekeyword extraction. A domain ontology can also assist in anontology-based keyword classification.

Embodiments of the present invention are based in part upon readydocumentation of work related activities in well-establishedorganizations. The digital generation entering the workforce, however,is giving rise to an increasing phenomenon of informality at work, withflattened organizational structure, home officing, and ad-hoc taskworkflow becoming common.

This presents both challenges and opportunities. On the one hand,work-based competency management is more important then ever before toensure a healthy growth of companies. On the other hand, the assumptionsenjoyed working with “traditional” companies may not be applicable inmore dynamic ones. Evaluation with both types of organizations may benecessary.

From a more technical perspective, integrating seamlessly withenterprise information platform is useful. Accordingly, schemesaccording to embodiments of the present invention may be extended tointerface with proprietary information systems.

Based on the above disclosure and the following claims, otherarrangements, embodiments, implementations and equivalents will beevident to those skilled in the art and may be employed withoutdeparting from the spirit and scope of the invention as defined by theclaims.

What is claimed is:
 1. A computer-implemented method comprising:receiving a written description that at least specifies actionscomprising a job task; performing text analysis and term extraction onthe written description to produce extracted tags; refining theextracted tags by clustering to produce refined tags; describing anenterprise resource using the refined tags; storing the refined tagsassociated with the enterprise resource in a data store of taggedenterprise resources; and managing resources in the enterprise for atask to be performed in the enterprise, including specifying one or morerefined tags stored in the data store of tagged enterprise resources toidentify the resources.
 2. The method of claim 1 wherein the writtendescription refers to the enterprise resource.
 3. The method of claim 2further comprising storing the refined tags associated with the jobtask.
 4. The method of claim 2 wherein the enterprise resource comprisesa human being.
 5. The method of claim 2 wherein the enterprise resourcecomprises a non-human resource.
 6. The method of claim 1 furthercomprising preprocessing the written description with natural languageprocessing methods prior to the extracting.
 7. The method of claim 1wherein the written description is linked to the job task through taskpatterning.
 8. The method of claim 1 wherein the performing furtherincludes one or more of: analyzing resources of the enterprise using therefined tags; and a recommendation of resources of the enterprise usingthe refined tags for a task to be performed in the enterprise.
 9. Anon-transitory computer readable storage medium embodying a computerprogram for performing a method, said method comprising: receiving awritten description that at least specifies actions comprising a jobtask; performing text analysis and term extraction on the writtendescription to produce extracted tags; refining the extracted tags byclustering to produce refined tags; describing an enterprise resourceinvolved in the job task using the refined tags; storing the refinedtags associated with the enterprise resource in a data store of taggedenterprise resources; and managing resources in the enterprise for atask to be performed in the enterprise, including specifying one or morerefined tags stored in the data store of tagged enterprise resources toidentify the resources.
 10. The non-transitory computer readable storagemedium of claim 9 wherein the written description refers to theenterprise resource.
 11. The non-transitory computer readable storagemedium of claim 10 wherein the method further comprises storing therefined tags associated with the job task.
 12. The non-transitorycomputer readable storage medium of claim 10 wherein the enterpriseresource comprises a human being.
 13. The non-transitory computerreadable storage medium of claim 10 wherein the enterprise resourcecomprises a non-human resource.
 14. The non-transitory computer readablestorage medium of claim 9 wherein the method further comprisespreprocessing the written description with natural language processingmethods prior to the extracting.
 15. A computer system comprising: oneor more processors; a software program, executable on said computersystem, the software program configured to: receive a writtendescription that at least specifies actions comprising to a job task;perform text analysis and term extraction on the written description toproduce extracted tags; refine the extracted tags by clustering toproduce refined tags; describe an enterprise resource involved in thejob task using the refined tags; store the refined tags associated withthe enterprise resource in a data store of tagged enterprise resources;and managing resources in the enterprise for a task to be performed inthe enterprise, including specifying one or more refined tags stored inthe data store of tagged enterprise resources to identify the resources.16. The computer system of claim 15 wherein the written descriptionrefers to the enterprise resource.
 17. The computer system of claim 16wherein the method further comprises storing the refined tags associatedwith the job task.
 18. The computer system of claim 16 wherein theenterprise resource comprises a human being or a non-human resource. 19.The computer system of claim 15 wherein the method further comprisespreprocessing the written description with natural language processingmethods prior to the extracting.
 20. The computer system of claim 15wherein the written description is linked to the job task through taskpatterning.